Power System Reactive Power Optimization Based On Improved Particle Swarm Optimization Algorithm

The optimization of power system reactive power is significant for the safe and economic operation of the power network, and it is an effective method to reduce the network loss and improve the voltage profile. It has the great significance to study the problem of reactive power optimization both in theory and practical application. In this paper, a research has been made on the reactive power system optimization based on the improved particle swarm optimization algorithm, and achieved satisfactory results.Reactive power optimization problem has a lot of variables and restrictions, is a nonlinear optimization problem. The control variables and states variables include continuous and discrete variables, so the process of optimization is very bother. Particle swarm algorithm is an intelligence optimization algorithm. The algorithm has the advantages of robust, astringency, currency. You don’t need to know the exact math model. So it is suitable to apply to the strong nonlinear optimization problems that are difficult to solve by the tradition’s search methods. However, the shortcoming of particle swarm optimization is easily plunging into the local minimum. In order to overcome the disadvantage, a method based on bee evolution modifying particle swarm optimization is presented. In the new algorithm, random drones are introduced for increase the diversity and guide the particles out of the local optimal solution.This paper introduces the reactive power optimization’s background, present research and signification, summarizes the methods of the classical methods and the intelligent method for the reactive optimization, and analyzes the advantages and disadvantages of these methods. The idea, principle and characteristic of reactive power optimization are researched. Taken generators terminal voltage, the transformer ratio and the reactive compensation capacities as control variables, a mathematical model is established to obtain minimization of network loss with satisfying constraints of power flow and security. After studying the principle, process and improved ideas of the particle swarm optimization algorithm, particle swarm optimization and bee evolution modifying particle swarm optimization are applied to IEEE6, IEEE14, IEEE30-bus system and Benteng substation of Daqing city for reactive power optimization. The results show that the bee evolution modifying particle swarm optimization algorithm is correct and effective in power system reactive power optimization compared to the other optimization algorithms.